A fundamental goal of automotive heating, ventilating, and air conditioning (HVAC) systems is to make vehicle occupants comfortable. To achieve this goal, it is important that the design of the control system that establishes cabin conditions takes into account the relationship between comfort and the variables that affect comfort. Human comfort is a complex reaction, involving physical, biological, and psychological responses to the given conditions. Because of this complexity, the engineer must consider many variables and their possible interaction in the design strategy of such a control system or controller.
In an attempt to measure and control the many variables that affect comfort, modern automotive HVAC systems have many sensors and control actuators. A typical system might have a temperature sensor inside the cabin, one measuring ambient temperature outside and others measuring various temperatures of the system internal workings. The occupant may have some input to the system via a set point or other adjustment. Additional sensors measuring sun heating load, humidity, etc. might be available to the system. The set of actuators might include a variable speed blower, some means for varying air temperature, ducting and doors to control the direction of air flow and the ratio of fresh to recirculated air.
It falls to the controller to sort out the range of possible conditions, determine what is needed to achieve comfort, and coordinate the control of the set of actuators available. This multiple input, multiple output control problem does not fall into any convenient category of traditional control theory. The performance criterion, comfort, is not some well defined formula but a sometimes inconsistent goal, empirically determined. In particular, comfort control is not the same as temperature control. The response of the system as well as the relationship between system variables and desired performance, comfort, is rarely linear. Also, it is important to note that despite all the actuators and variables available for control, there may exist conditions under which comfort may not be achievable.
Due to practical considerations of size, energy consumption, cost and the wide conceivable range of conditions that automobiles are exposed to, the system plant may simply not be able to supply what is needed. All these considerations lead to a control problem that is far from what is usually encountered in traditional control theory.
In the face of these difficulties, most control system designs have used what is familiar--linear control--and supplemented it by patched-in specific responses to handle special circumstances where necessary. In other words, typical automobile automatic climate control systems use linear proportional control to maintain a comfortable interior environment. However, there are two significant limitations of linear proportional control when viewed from the standpoint of an occupant's subjective comfort: first, there are certain control situations in any HVAC system that are inherently nonlinear, and second, it is not possible to realize occupant comfort merely by maintaining proximity to a desired temperature as described in greater detail hereinbelow.
The design of a typical HVAC climate control system starts with the need to provide acceptable occupant comfort levels under the most extreme high and low ambient conditions that a vehicle might encounter. For these conditions, the control system is requesting the HVAC unit to operate at peak output in one direction or the other. Design considerations center around plant capacity and the efficiency of heat transfer in order to handle these extremes. The contol system is effectively saturated until one or more of the input signals indicate that some level of comfort control is achievable.
It is at this point that the system begins to moderate its control of blower speed, the location of discharge air (mode of operation), and the relative blend of cooled and heated air. The simplest approach to control in this region is to have the control follow a straight line between the two extremes. Such a linear control algorithm adjusts the outputs in an appropriate manner and its parameters are easy to determine based on the points of onset of the two extreme regions. With a well defined HVAC system and enough developmental evaluation time, one can tune the coefficients to provide acceptable levels of comfort for a variety of operating conditions. The linear approach is quite well understood and easy to implement. For a small microprocessor-based controller, its essence is captured in a few lines of code.
The linear approach has obvious limitations when dealing with nonlinear situations. All HVAC systems behave nonlinearly in various regions of their operation. The transfer of heat as a function of blower speed is nonlinear. The onset of any plant output limitation affects desired response in a nonlinear fashion. Factors affecting plant limitations may be tracked via additional sensors--for example, engine coolant temperature (ECT) correlates with heater core temperature--but again, the relationship is nonlinear. The usual approach to handling special nonlinear situations is to use logic-based modification of the usual linear strategy when these situations are detected. Thus, in cold weather, when ECT is below a certain threshold indicating that the heater core cannot warm the cabin, the blower would be shut off.
This particular solution to the problem of nonlinearities creates problems of its own. In the case of the binary ECT threshold switch, interaction with the linear strategy leads to difficulties. When the threshold ECT is passed, the switch turns on the blower. Since the car is cold, the blower immediately goes to its highest setting and creates two problems. The first is the noise level produced by the blower operating full out. The second problem is that all the residual cold air in the system is blown directly onto the customer's feet causing discomfort.
In addition to the current difficulties, new vehicle lines create additional problems that are not easy to overcome. The reduction in interior and under hood package space in current vehicle designs has caused the transfer function for discharge temperature to be even more nonlinear, especially when operating at the extremes of ambient temperature.
The response of crisp (as opposed to fuzzy) logic in a control strategy does not fit well when human comfort is the goal. Abrupt changes in environment are not perceived favorably by most people. It is true that the effect of sudden changes occasioned by crisp logic transitions may be masked via input or output filtering. Also, some of the resulting conditions may not be experienced by the occupant as a level of discomfort. For example, heater warmup, linear or nonlinear, has no effect on comfort on a hot day with the system at maximum cooling.
Fuzzy Logic Approach
As previously mentioned, the description of comfort for most people is expressed in terms that are not particularly precise. If one asks people how they describe their comfort, we get answers such as "slightly cold", "fine", or "very hot." A person's comfort can easily be phrased in such vague terms but it is more difficult to interpret these expressions quantitatively. The imprecise nature of comfort description leads to the use of fuzzy logic in specifying a strategy for comfort control. Fuzzy logic provides procedures to incorporate knowledge expressed vaguely and yet arrive at a definite, calculable answer.
Fuzzy logic is a methodology for handling knowledge that contains some uncertainty or vagueness. The foundations of fuzzy logic were set forth in the 1960s by L. A. Zadeh in his paper entitled "Fuzzy Sets", INFORM. CONTR., 8 pp. 338-353, 1965.
In current engineering application, fuzzy logic is most often found in control problems in the form of a particular procedure, called "max-min" fuzzy inference as described by Ebrahim Mamdani in his paper entitled "Application of Fuzzy Logic to Approximate Reasoning Using Linguistic Synthesis", IEEE TRANSACTIONS ON COMPUTERS, (1977) C-26, No. 12, pp. 1182-1191. This procedure incorporates approximate knowledge of appropriate control response for different circumstances into sets of rules for calculating a particular control action. The rules are expressed in terms of "IF (situation holds), THEN (take consequent control action)". The degree to which a particular consequent action is taken depends on the degree to which its corresponding conditions hold. The linguistic expression of a situation or consequent control action is translated into a definite calculation via specified membership functions. A membership function quantifies what is meant by a phrase such as "The temperature is high" by defining the degree of membership in the class, "high", depending on the value of the input variable, temperature.
U.S. Pat. No. 5,148,977 uses an infrared sensor to measure wall temperature and uses that value to modify room temperature using fuzzy logic.
U.S. Pat. No. 5,156,013 discloses a control device for an adsorption refrigerator including a generator. A heating amount of the generator is controlled by a fuzzy logic calculation. The fuzzy logic algorithm utilizes a standard matrix approach choice of input and output membership functions to compute an output value in a standard fashion.
U.S. Pat. No. 4,914,924 senses driver intent along with air conditioning system state to balance the conflicting tradeoffs of powertrain performance versus air conditioning performance. Driver intent is sensed via a throttle position sensor and a preference switch. Air conditioning system state is sensed via the standard complement of sensors found in most systems. Standard fuzzy logic inference is used directly to sort out the tradeoffs as well as performing the usual functions of climate control.